import copy
from math import sqrt
import random
import matplotlib.pyplot as plt
 
#无向图
sample = [
    [1,41,94],
    [2,37,84],
    [3,54,67],
    [4,25,62],  
    [5,7,64],
    [6,2,99],
    [7,68,58],
    [8,71,44],
    [9,54,62],
    [10,83,69],
    [11,64,60],
    [12,18,54],
    [13,22,60],
    [14,83,46],
    [15,91,38],
    [16,25,38],
    [17,24,42],
    [18,58,69],
    [19,71,71],
    [20,74,78],
    [21,87,76],
    [22,18,40],
    [23,13,40],
    [24,82,7],
    [25,62,32],
    [26,58,35],
    [27,45,21],
    [28,41,26],
    [29,44,35],
    [30,4,50],
]
 
vertex_count = len(sample)    #顶点数
 
def draw1(ls1,ls2):
    plt.plot(ls1,ls2,label=u'alpha=4 beta=4')
    plt.show()
 
def draw(ls):
    x = []
    y = []
    for i in ls:  
        x.append(sample[i-1][1])
        y.append(sample[i-1][2])
    plt.xlim(0, 100)  # 限定横轴的范围
    plt.ylim(0, 100)  # 限定纵轴的范围
    plt.plot(x, y, marker='o', mec='r', mfc='w',label=u'alpha=5 beta=1')
    plt.legend()  # 让图例生效
    plt.margins(0)
    plt.subplots_adjust(bottom=0.15)
    plt.xlabel(u"X") #X轴标签
    plt.ylabel("Y") #Y轴标签
    plt.title("PATH") #标题
    plt.show()
 
def city_dist(sample,city1,city2):
    if city1 == city2:
        return 9999
    loc1 = []
    loc2 = []
    findtag = 0
    dist = 0
    for i in range(len(sample)):
        if city1 == sample[i][0]:
            loc1.append(sample[i][1])
            loc1.append(sample[i][2])
            findtag += 1
        if city2 == sample[i][0]:
            loc2.append(sample[i][1])
            loc2.append(sample[i][2])
            findtag += 1
        if findtag == 2:
            break
    dist = sqrt(((loc1[0]-loc2[0])**2 + (loc1[1]-loc2[1])**2))
    return dist
 
def evalute(sample,individual):
    distance = 0
    for i in range(len(individual)-1):   # vertex_count = len(individual)-1
        distance += city_dist(sample,individual[i], individual[i+1])
    return distance
 
def find_pheromone(pheromone,city1,city2):
    #信息素表示 [1,2,a] 即城市1与2之间的信息素为a
    for i in range(len(pheromone)):
        if city1 in pheromone[i] and city2 in pheromone[i]:
            return pheromone[i][2]
    print("return 0")
    return 0
 
def update_pehromone(pheromone,city1,city2,new_pehromone):      #更新信息素
    for i in range(len(pheromone)):
        if city1 in pheromone[i] and city2 in pheromone[i]:
            pheromone[i][2] = 0.9 * pheromone[i][2] + new_pehromone
 
def get_sum(cur_city,pheromone,allowed_city,alpha,beta):
    sum = 0
    for i in range(len(allowed_city)):
        sum += (find_pheromone(pheromone,cur_city,allowed_city[i])**alpha) * (city_dist(sample,cur_city,allowed_city[i])**beta)
    return sum
 
def AntColony_Algorithm(sample):
    #重要参数
    ant_count = 26  #蚁群个体数
    alpha = 5
    beta = 1
    loop_count = 100    #迭代次数
 
    ant_colony = []
    ant_individual = []
    allowed_city = []     #待选城市
    city = []
    p = []      #记录选择某一城市概率
    pheromone = []
    draw_ls1 = []
    draw_ls2 = []
    best_dist = 9999
    best_route = []
 
    for i in range(1,vertex_count+1):   #初始化信息素
        for j in range(i+1,vertex_count+1):
            pheromone.append([i,j,100])  #信息素初始化不能为0
    for i in range(1,vertex_count+1):   #初始化城市和概率
        city.append(i)
 
    #进行100次迭代
    for i in range(loop_count):
        if i%20 == 0 :
            print(i)
            draw(best_route)
        draw_ls1.append(i)
        #随机产生蚂蚁起始点
        start_city = []
        for j in range(ant_count):
            start_city.append(random.randint(1,len(city)))
        for j in range(len(start_city)):
            ant_individual.append(start_city[j])
            ant_colony.append(copy.deepcopy(ant_individual))
            ant_individual.clear()
        #所有蚂蚁完成遍历
        for singal_ant in range(ant_count):
            #单个蚂蚁完成路径
            allowed_city = copy.deepcopy(city)
            allowed_city.remove(ant_colony[singal_ant][0])
            for m in range(vertex_count): #确定了起始城市，循环次数-1
                cur_city = ant_colony[singal_ant][-1]
                #单个蚂蚁遍历所有城市以确定下一城市
                for j in range(len(allowed_city)):
                    probability = ((find_pheromone(pheromone,cur_city,allowed_city[j])**alpha) * (city_dist(sample,cur_city,allowed_city[j])**beta))/get_sum(cur_city,pheromone,allowed_city,alpha,beta)
                    p.append(probability)  
                #求累积概率
                cumulative_probability = [0]
                for j in range(len(p)):
                    cumulative_probability.append(cumulative_probability[j] + p[j])
                #自然选择  轮盘赌概率选择下一城市
                temp_random = random.random()     #产生(0,1)随机数
                for j in range(1,len(cumulative_probability)):
                    if temp_random > cumulative_probability[j-1] and temp_random < cumulative_probability[j]:
                        ant_colony[singal_ant].append(allowed_city[j-1])  #在单个蚂蚁中添加被选择的下一城市
                        del allowed_city[j-1]
                        break
                p.clear()   
            ant_colony[singal_ant].append(ant_colony[singal_ant][0])   
        #计算每只蚂蚁的路径长度并更新所有蚂蚁路径上的信息素
        for j in range(ant_count):
            if evalute(sample,ant_colony[j]) < best_dist:
                    best_dist = evalute(sample,ant_colony[j])
                    best_route = copy.deepcopy(ant_colony[j])
            for k in range(len(ant_colony[j])-1):
                update_pehromone(pheromone,ant_colony[j][k],ant_colony[j][k+1],10000/city_dist(sample,ant_colony[j][k],ant_colony[j][k+1]))
        draw_ls2.append(best_dist)
        ant_colony.clear()
        
    print("outcome")
    print(pheromone)
    print(best_route)
    print(best_dist)
    draw(best_route)
    draw1(draw_ls1,draw_ls2)
 
AntColony_Algorithm(sample)